大学生在线学习监控系统的设计与实现

Design and Implementation Analysis of Online Learning Monitoring System for College Students

摘要: 网络教学已成为大学教育的主流，然而由于在线学习师生时空分离的特性，如何实时了解学生的学习情况是函待解决的问题。大学生在线学习监控系统基于Python和PyQt5构建，以云数据库MySQL作为数据存储平台，并整合百度云人脸识别API等技术设计实现。采用端到端全卷积神经网络人脸检测器和基于端到端多patch深度神经网络的度量学习技术进行人脸检测，实现了比人眼更加精准的识别效果。系统通过监控学生主机的活动进程、浏览历史记录、在线签到等反馈学生的在线学习行为，再通过评分机制激发学生的学习积极性，提升了在线学习的效率。

Abstract: Online teaching has become the mainstream of university education. However, due to the characteristics of time and space separation between teachers and students in online learning, how to understand students’ learning status in real time is a problem that needs to be solved. The online learning monitoring system for college students is built based on Python and PyQt5, using the cloud database MySQL as the data storage platform, and integrating Baidu Cloud Face Recognition API and other technologies for design and implementation. It uses an end-to-end fully convolutional neural network face detector and a metric learning technology based on an end-to-end multi-patch deep neural network for face detection, achieving a more accurate recognition effect than the human eye. The system provides feedback on students’ online learning behavior by monitoring the activity progress of students’ hosts, browsing history, online check-ins, etc., and then stimulates students’ learning enthusiasm through the scoring mechanism, improving the efficiency of online learning.